Instructions to use AMKCode/Phi-3.5-mini-instruct-q4f16_1-MLC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AMKCode/Phi-3.5-mini-instruct-q4f16_1-MLC with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="AMKCode/Phi-3.5-mini-instruct-q4f16_1-MLC") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("AMKCode/Phi-3.5-mini-instruct-q4f16_1-MLC", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use AMKCode/Phi-3.5-mini-instruct-q4f16_1-MLC with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "AMKCode/Phi-3.5-mini-instruct-q4f16_1-MLC" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AMKCode/Phi-3.5-mini-instruct-q4f16_1-MLC", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/AMKCode/Phi-3.5-mini-instruct-q4f16_1-MLC
- SGLang
How to use AMKCode/Phi-3.5-mini-instruct-q4f16_1-MLC with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "AMKCode/Phi-3.5-mini-instruct-q4f16_1-MLC" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AMKCode/Phi-3.5-mini-instruct-q4f16_1-MLC", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "AMKCode/Phi-3.5-mini-instruct-q4f16_1-MLC" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AMKCode/Phi-3.5-mini-instruct-q4f16_1-MLC", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use AMKCode/Phi-3.5-mini-instruct-q4f16_1-MLC with Docker Model Runner:
docker model run hf.co/AMKCode/Phi-3.5-mini-instruct-q4f16_1-MLC
library_name: mlc-llm base_model: microsoft/Phi-3.5-mini-instruct tags: - mlc-llm - web-llm
AMKCode/Phi-3.5-mini-instruct-q4f16_1-MLC
This is the Phi-3.5-mini-instruct model in MLC format q4f16_1.
The conversion was done using the MLC-Weight-Conversion space.
The model can be used for projects MLC-LLM and WebLLM.
Example Usage
Here are some examples of using this model in MLC LLM. Before running the examples, please install MLC LLM by following the installation documentation.
Chat
In command line, run
mlc_llm chat HF://mlc-ai/AMKCode/Phi-3.5-mini-instruct-q4f16_1-MLC
REST Server
In command line, run
mlc_llm serve HF://mlc-ai/AMKCode/Phi-3.5-mini-instruct-q4f16_1-MLC
Python API
from mlc_llm import MLCEngine
# Create engine
model = "HF://mlc-ai/AMKCode/Phi-3.5-mini-instruct-q4f16_1-MLC"
engine = MLCEngine(model)
# Run chat completion in OpenAI API.
for response in engine.chat.completions.create(
messages=[{"role": "user", "content": "What is the meaning of life?"}],
model=model,
stream=True,
):
for choice in response.choices:
print(choice.delta.content, end="", flush=True)
print("\n")
engine.terminate()
Documentation
For more information on MLC LLM project, please visit our documentation and GitHub repo.
Model tree for AMKCode/Phi-3.5-mini-instruct-q4f16_1-MLC
Base model
microsoft/Phi-3.5-mini-instruct